Material data processing method and system

Abstract

The present application relates to the field of automobile parts data processing, more particularly, the present application relates to a kind of material data processing method and system.The method comprises: obtaining the material data of several automobile parts;Material data is clustered to obtain final clustering cluster, and the material data of automobile parts is managed according to final clustering cluster.Through the technical scheme of the present application, the accuracy of the material data clustering of automobile parts can be improved, and the precision of data processing result is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of automobile parts data processing. More specifically, the present application relates to a material data processing method and system. BACKGROUND

[0002] With the rapid development of the automotive industry, the material data processing method and system of automobile parts become increasingly important. Automobile manufacturing involves the production and assembly of thousands of parts, and ensuring the quality and supply chain management of parts is crucial for the performance, safety and cost control of the whole vehicle. In order to cope with these challenges, more and more automobile manufacturers and parts suppliers begin to introduce advanced technologies such as Internet of Things, artificial intelligence and big data analysis, and build intelligent material data processing systems. These systems not only improve the work efficiency and supply chain transparency, but also help enterprises maintain an advantage in fierce market competition and promote the progress towards lean production and continuous innovation.

[0003] The existing patent application file with the publication number CN108733003A discloses a rotary part process time prediction method and system based on kmeans clustering algorithm, wherein the application file adopts an initial cluster center calculation method, a distance calculation method and an update cluster center method to obtain clustering results and cluster centers under different K values, calculates SP / DB indexes under different K values, takes the K value corresponding to the minimum SP / DB, the cluster center and the clustering result, which is the final K value, the final clustering result and the final cluster center.

[0004] However, when analyzing the material data of automobile parts by kmeans clustering, there may be many deviations in the data collection process, and the data with large deviation degree will cause the center point in the clustering algorithm to deviate, thereby affecting the clustering result and further leading to inaccurate data processing result. SUMMARY

[0005] To solve the problem of inaccurate data processing result, the present application proposes a material data processing method and system.

[0006] In a first aspect, the present application discloses a material data processing method, comprising: obtaining material data of a plurality of automobile parts; clustering the material data to obtain final clustering clusters, and managing the material data of the automobile parts according to the final clustering clusters; wherein the final clustering comprises: presetting a number of cluster center points, obtaining a position of each cluster center point, taking any material data as a target point, for any iteration, calculating a dispersion degree according to a preset neighborhood range of the target point, and calculating a belonging possibility of the target point in any iteration; taking a ratio of the belonging possibility and the dispersion degree as an influence weight of the target point, traversing to obtain the influence weight of each material data, updating the position of the cluster center point, and obtaining the final clustering clusters in response to the iteration satisfying a stop condition.

[0007] By clustering analysis, the material data of the automobile parts is managed, the influence weight of the target point is calculated according to the belonging possibility and the dispersion degree, and the adaptive adjustment of the data points to the cluster center is effectively realized. In each iteration, the material data is dynamically evaluated according to the dispersion and the belonging possibility of the neighborhood, and the cluster center is gradually close to the real distribution of the data. Through this weighted adjustment mode, the final clustering cluster can more accurately reflect the actual characteristics of the material data of the automobile parts, thereby providing more accurate classification and optimization in material management, improving the efficiency of inventory management, production scheduling and quality control, and reducing resource waste and potential management errors.

[0008] Preferably, the obtaining of the position of each cluster center point comprises: taking the center point of all material data as a first cluster center point, taking the material data farthest from the first cluster center point as a first reference point, selecting the midpoint of the first cluster center point and the reference point as a second cluster center point, obtaining a third cluster center point according to the first cluster center point and the second cluster center point, and obtaining all cluster center points in the same way; and obtaining the position of each cluster center point in response to the number of cluster center points reaching the preset number of cluster center points.

[0009] Not only improves the accuracy of the clustering result, but also helps to better classify, analyze and control the distribution of the material.

[0010] Preferably, the obtaining of the third cluster center point comprises: taking the material data with the maximum sum of distances from the first cluster center point and the second cluster center point as a second reference point; and selecting the midpoint of the first cluster center point, the second cluster center point and the second reference point as the third cluster center point.

[0011] Preferably, the discrete degree is obtained by: obtaining a neighborhood range of the target point, taking the material data in the neighborhood range except the target point as first neighborhood points, and taking the neighborhood points of the cluster center point of the cluster to which the target point belongs as second neighborhood points; calculating the absolute difference value between the number of the first neighborhood points and the number of the second neighborhood points, and taking the ratio of the absolute difference value to the number of the second neighborhood points as the discrete degree.

[0012] By calculating the difference value and the ratio of the number of the first neighborhood points and the number of the second neighborhood points, the stability of the target point in the cluster and the deviation degree from the cluster center can be quantified. Such a calculation method helps to identify abnormal data points deviating from the cluster center, reduces their influence on the clustering result, and thus improves the accuracy and robustness of the clustering result. Not only can the clustering process be optimized, but also the distribution characteristics of the material data in actual application can be better reflected.

[0013] Preferably, the discrete degree is obtained by: obtaining a neighborhood range of the target point, taking the material data in the neighborhood range except the target point as first neighborhood points, and taking the neighborhood points of the cluster center point of the cluster to which the target point belongs as second neighborhood points; taking the range of the material data in the first neighborhood points as a first range, and taking the range of the material data in the second neighborhood points as a second range, calculating the absolute difference value between the first range and the second range, and taking the ratio of the absolute difference value to the second range as the discrete degree.

[0014] By calculating the discrete degree of the target point and the material data in its neighborhood, the differences between the material data in different regions are quantified, which can effectively reveal the change and fluctuation trend of the material data.

[0015] Preferably, the belonging possibility satisfies the following relationship:

[0016] G m,i represents the belonging possibility of the target point i after the mth iteration, d m,i represents the Euclidean distance between the cluster center point of the cluster to which the target point i belongs and the target point i after the mth iteration, represents the mean value of the Euclidean distances between all the material data in the cluster to which the target point i belongs and the cluster center point after the mth iteration, E m,i,j and E m-1,i,j respectively represent the value of the cluster center point of the cluster to which the target point i belongs in the jth dimension after the mth iteration and after the (m-1)th iteration, D i,j represents the value of the target point i in the jth dimension, M represents the total number of iterations, and J represents the total number of dimensions of the material data.

[0017] The formula of the attribution possibility adjusts and optimizes the clustering result through multiple iterations, specifically, with the iteration, the distance between the target point and the current cluster center is calculated each time, and the attribution possibility is dynamically adjusted according to the position of the target point in the cluster and the change of the cluster center.

[0018] Preferably, the stop condition is that there is no change in the material data in the clustering cluster after the end of the two adjacent iterations.

[0019] In a second aspect, the present application discloses a material data processing system, comprising: a processor; and a memory, the memory stores computer instructions, when the computer instructions are run by the processor, the system executes the above-mentioned material data processing method.

[0020] The beneficial effects of the present application are:

[0021] The present application gradually optimizes the clustering result by presetting the number of cluster center points and updating the position of the cluster center points, ensures that each material data is reasonably allocated according to its distance from the cluster center and its neighborhood characteristics. By using the calculation of the influence weight, the importance of each material data in the clustering process can be effectively reflected, and the position of the cluster center point is continuously optimized through iteration, so that a stable clustering effect is achieved. This processing method based on clustering improves the classification accuracy of material data, effectively improves the efficiency and quality of material data processing. At the same time, through the measurement of the dispersion degree, the flexibility and adaptability of the clustering process are further improved, and the deviation of the clustering result is avoided. BRIEF DESCRIPTION OF DRAWINGS

[0022] The above and other objects, features and advantages of the exemplary embodiments of the present application will be more apparent from the following detailed description taken in conjunction with the accompanying drawings, in which several embodiments of the present application are shown by way of example, and wherein like reference numerals refer to like elements throughout. In the drawings:

[0023] Figure 1 is a flowchart of a material data processing method according to an embodiment of the present application. DETAILED DESCRIPTION

[0024] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0025] It should be understood that when the claims, the specification, and the drawings of the present application use terms such as "first", "second", etc., these terms are only used to distinguish different objects, and are not used to describe a specific order. The terms "include" and "contain" used in the specification and claims of the present application indicate the presence of the described features, whole, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, whole, steps, operations, elements, components and / or sets thereof.

[0026] The present application provides a material data processing method. As shown in Figure 1 The material data processing method comprises steps S1-S2, which are described in detail below.

[0027] S1, obtaining material data of a plurality of automobile parts.

[0028] It should be noted that the quality evaluation standards of parts in different positions of the vehicle will be different, which will be determined according to the function of the parts in the vehicle and the use environment. For example, engine-related components usually need to take torque, strength and high-temperature performance as the main standards, because these components bear greater pressure and high-temperature environment; while the body structure components such as doors, windows, etc. may pay more attention to impact resistance, corrosion resistance and tensile strength to ensure safety and durability; some electrical components may focus more on electrical performance and cold and heat resistance to meet the use requirements under different climate conditions.

[0029] In one embodiment, the material data of the automobile parts is collected, which can be single-dimensional data or multi-dimensional data. For example, the single-dimensional data is length or weight or torque. The multi-dimensional data is length and weight, or length and torque, or length, weight and torque.

[0030] S2, clustering the material data to obtain a final clustering cluster, and managing the material data of the automobile parts according to the final clustering cluster.

[0031] It should be noted that when the material data of the automobile parts is clustered by the Kmeans algorithm, the abnormal data may have a great influence on the calculation of the cluster center point, causing the center point to deviate from the true position of the clustered data points, so that the final clustering result is inaccurate. In order to solve this problem, the dispersion degree of each material data can be calculated to measure its stability in the clustering cluster, so as to determine its attribution degree. The material data with higher attribution degree has greater contribution to the cluster center point, while the material data with lower attribution degree (i.e. abnormal data) has relatively smaller influence. Based on this, a weight value can be assigned to each material data. On this basis, the cluster center point is recalculated by weighted average to improve the accuracy of clustering.

[0032] In one embodiment, the number of preset cluster center points is obtained, and the position of each cluster center point is obtained. It includes: taking the center point of all material data as the first cluster center point, taking the material data farthest from the first cluster center point as the first reference point, selecting the midpoint of the first cluster center point and the reference point as the second cluster center point, obtaining the third cluster center point according to the first cluster center point and the second cluster center point, and obtaining the third cluster center point includes:

[0033] The material data with the maximum sum of distances from the first cluster center point and the second cluster center point is taken as the second reference point. The midpoint of the first cluster center point, the second cluster center point and the second reference point is selected as the third cluster center point. The third cluster center point is actually the midpoint of the triangle formed by the first cluster center point, the second cluster center point and the second reference point.

[0034] Then the material data with the maximum sum of distances from the first cluster center point, the second cluster center point and the third cluster center point is taken as the third reference point. The midpoint of the first cluster center point, the second cluster center point, the third cluster center point and the third reference point is selected as the fourth cluster center point. The fourth cluster center point is actually the midpoint of the quadrilateral formed by the first cluster center point, the second cluster center point, the third cluster center point and the third reference point.

[0035] Similarly, all cluster center points are obtained.

[0036] In response to the number of cluster center points reaching the preset number of cluster center points, the position of each cluster center point is obtained.

[0037] Taking any material data as a target point, for any iteration, the neighborhood range of the target point is obtained, the material data in the neighborhood range except the target point is taken as the first neighborhood point, and the neighborhood point of the cluster center point of the cluster to which the target point belongs is taken as the second neighborhood point. The absolute difference value of the number of first neighborhood points and the number of second neighborhood points is calculated, and the ratio of the absolute difference value to the number of second neighborhood points is taken as the degree of dispersion.

[0038] By calculating the difference and its ratio of the number of first neighborhood points and second neighborhood points, the stability of the target point in the cluster and the deviation degree from the clustering center can be quantified. Such calculation method helps to identify abnormal data points deviating from the clustering center, reduces their influence on the clustering result, and thus improves the accuracy and robustness of the clustering result. Not only can the clustering process be optimized, but also the distribution characteristics of material data in actual application can be better reflected.

[0039] For example, the neighborhood range is set to the result of the dimension number of material data mapped by an exponential function, and the specific value can be set by those skilled in the art.

[0040] The belonging possibility of the target point in any iteration is calculated, and the belonging possibility satisfies the relationship:

[0041] G m,i represents the belonging possibility of the target point i after the mth iteration, d m,i represents the Euclidean distance between the cluster center point of the cluster to which the target point i belongs and the target point i after the mth iteration, represents the average of the Euclidean distances between all material data in the cluster to which the target point i belongs and the cluster center point after the mth iteration, E m,i,j and E m-1,i,j respectively represent the value of the cluster center point of the cluster to which the target point i belongs in dimension j after the mth iteration and the (m-1)th iteration, D i,j represents the value of the target point i in dimension j, M represents the total number of iterations, and J represents the total number of dimensions of the material data.

[0042] The formula of the belonging possibility adjusts and optimizes the clustering result through multiple iterations. Specifically, with the progress of iteration, the distance between the target point and the current cluster center is calculated each time, and the belonging possibility is dynamically adjusted according to the position of the target point in the cluster and the change of the cluster center. This process can capture the belonging change of the target point in different iterations, reflecting the stability of the target point relative to the cluster center. With the increase of the number of iterations, the cluster center gradually tends to be stable, and the accuracy of the clustering result also improves.

[0043] The ratio of the belonging possibility and the dispersion degree is taken as the influence weight of the target point, and the influence weight of each material data is obtained by traversal. The position of the cluster center point is updated according to the influence weight, and the final clustering cluster is obtained in response to the iteration satisfying the stopping condition. The stopping condition is that there is no change in the material data in the clustering cluster after the end of the adjacent two iterations.

[0044] Specifically, in each iteration process, first, according to the current clustering cluster, a weight, i.e. the influence weight, is assigned to each material data, which reflects the influence degree of the material data in the clustering cluster. Next, according to the influence weight of each material data, the weighted average value of all data points in the same clustering cluster is calculated to obtain the weighted cluster center point. Then, in each iteration, the influence weight of each material data is recalculated, and the weighted cluster center point is updated according to the new influence weight. The center position of the clustering cluster is adjusted constantly to obtain a more stable and accurate final clustering cluster.

[0045] According to the final clustering cluster, the material data of the automobile parts belonging to the same category can be extracted, and then in-depth grouping analysis of the material of each category can be carried out. By analyzing the characteristics of the material data in each category, combined with historical data and quality standards, the quality performance of each group of automobile parts can be predicted.

[0046] In another embodiment, the dispersion degree further comprises: obtaining a neighborhood range of the target point, taking the material data in the neighborhood range except the target point as first neighborhood points, and taking the neighborhood points of the cluster center point of the cluster to which the target point belongs as second neighborhood points; taking the range of the material data in the first neighborhood points as a first range, taking the range of the material data in the second neighborhood points as a second range, calculating the absolute difference value of the first range and the second range, and taking the ratio of the absolute difference value to the second range as the dispersion degree.

[0047] By calculating the dispersion degree of the target point and the material data in the neighborhood thereof, the difference of the material data between different regions is quantified, and the change and fluctuation trend of the material data can be effectively revealed.

[0048] The embodiment of the present application further discloses a material data processing system comprising a processor and a memory, and the memory stores computer program instructions, which realize the material data processing method according to the present application when executed by the processor.

[0049] The above system further comprises a communication bus and a communication interface and other components well known to those skilled in the art, the settings and functions of which are known in the art, and thus will not be described here.

[0050] In the present application, the aforementioned memory can be any tangible medium containing or storing a program, which can be used or combined with an instruction execution system, device or apparatus. For example, the computer readable storage medium can be any suitable magnetic storage medium or magneto-optical storage medium, such as resistive random access memory (RRAM), dynamic random access memory (DRAM), static random access memory (SRAM), enhanced dynamic random access memory (EDRAM), high bandwidth memory (HBM), hybrid memory cube (HMC) and the like, or any other medium that can be used to store the required information and can be accessed by an application program, module or both. Any such computer storage medium can be part of the device or accessible or connectable to the device.

[0051] While the present specification has shown and described a number of embodiments of the present application, it is to be understood that those skilled in the art will be able to devise numerous alterations, modifications and equivalents of the embodiments without departing from the spirit and scope of the application. It is the intention that all such alterations, modifications and equivalents be included within the scope of the present application.

[0052] The above are preferred embodiments of the present application, and are not intended to limit the protection scope of the present application, therefore: all equivalent changes made according to the structure, shape, principle of the present application should be covered within the protection scope of the present application.

Claims

1. A material data processing method, characterized in that, include: Obtain material data for several automotive parts; The material data is clustered to obtain the final clusters, and the material data of automotive parts is managed based on the final clusters. The process of obtaining the final clustering includes: setting the number of cluster centers, obtaining the position of each cluster center, taking any material data as the target point, calculating the degree of dispersion based on the preset neighborhood range of the target point for any iteration, and calculating the probability of the target point belonging to any iteration. The ratio of the probability of belonging to the target point to the degree of dispersion is used as the influence weight of the target point. The influence weight of each material data is obtained by traversing the data. The position of the cluster center point is updated. The final cluster is obtained when the iteration meets the stopping condition. The probability of belonging satisfies the following relation: , Indicates the first Target point after the second iteration The possibility of ownership, Indicates the first Target point after the second iteration Cluster center and target point of the cluster The Euclidean distance between them Indicates the first Target point after the second iteration The mean Euclidean distance between all material data in the same cluster and the cluster center. and They represent the first time. After the nth iteration and the th Target point after the second iteration The cluster center of the cluster belongs to the dimension The value, Indicates the target point In dimensions The value, Indicates the total number of iterations. This indicates the total number of dimensions in the material data.

2. The material data processing method according to claim 1, characterized in that, The process of obtaining the location of each cluster center point includes: The center point of all material data is taken as the first cluster center point. The material data farthest from the first cluster center point is taken as the first reference point. The midpoint between the first cluster center point and the reference point is selected as the second cluster center point. The third cluster center point is obtained based on the first and second cluster center points. Similarly, all cluster center points are obtained. In response to the number of cluster centers reaching the preset number of cluster centers, the position of each cluster center is obtained.

3. The material data processing method according to claim 2, characterized in that, The process of obtaining the third cluster center point includes: The material data with the largest sum of distances to the first cluster center point and the second cluster center point is used as the second reference point; The midpoint between the first cluster center point, the second cluster center point, and the second reference point is selected as the third cluster center point.

4. The material data processing method according to claim 1, characterized in that, Obtaining the degree of dispersion includes: Obtain the neighborhood range of the target point, take the material data other than the target point within the neighborhood range as the first neighborhood point, and take the neighborhood point of the cluster center of the cluster to which the target point belongs as the second neighborhood point; Calculate the absolute difference between the number of points in the first neighborhood and the number of points in the second neighborhood, and use the ratio of the absolute difference to the number of points in the second neighborhood as the degree of dispersion.

5. The material data processing method according to claim 1, characterized in that, Obtaining the degree of dispersion also includes: Obtain the neighborhood range of the target point, take the material data other than the target point within the neighborhood range as the first neighborhood point, and take the neighborhood point of the cluster center of the cluster to which the target point belongs as the second neighborhood point; The range of material data in the first neighborhood point is taken as the first range, and the range of material data in the second neighborhood point is taken as the second range. The absolute difference between the first range and the second range is calculated, and the ratio of the absolute difference to the second range is taken as the degree of dispersion.

6. The material data processing method according to claim 1, characterized in that, The stopping condition is that after two consecutive iterations, the material data within the cluster remains unchanged.

7. A material data processing system, characterized in that, include: Processor; and A memory storing computer instructions that, when executed by a processor, cause the system to perform a material data processing method according to any one of claims 1-6.

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